Analysis of blood vessels from retinal images has clinical significance since retinal blood vessels are the first ones that show the symptoms of several pathologies, such as arterial hypertension, arteriosclerosis, and other systemic vascular diseases.
In order to detect such symptoms and to perform diagnosis, it is typically required to perform a series of image processing tasks. Currently available retinal analysis systems require significant manual input from the medical personnel, are subjective, and analysis is time-consuming and prone to error. Accurate, objective, reliable, and automatic (or semi-automatic) retinal image medical systems are currently not available with the performance required for routine clinical use.
As an example, the computation of the retina arteriovenous ratio (AVR), that is, the relation between afferent and efferent blood vessels of the retinal vascular tree, is significant in order to diagnose diseases and evaluate their consequences. Due to the unavailability of commercial retinal image analysis systems with precise and robust estimation of the AVR metric, analysis of the AVR is usually computed by a tedious and time consuming manual process. This the results in more expensive, subjective, and ophthalmologist-dependent AVR computations. Similarly, other clinical parameters derived from retinal image analysis are tedious and their manual computation is subjective.